Quick Info
Coyotes (Canis lantrans) data on three individuals near Albany, NY, during the spring and summer of 2001, were used as examples for performing home range analyses.
Data comes from:
Bogan, D.A. 2004 Eastern coyote (Canis latrans) home range, habitat selection, and survival rates in the suburban Albany Pine Bush landscape of New York. MA Thesis. State University of New York at Albany.

Dataset
Convert Lat & Long to UTM Because I’m stubborn
Lets see if the dataset has any outliers, and get a rough view of the locations in a map.
Here, I will do a quick map on the three coyote individuals with a basemap from Open Street Map.
utm_points <- cbind(coyote$utm.easting, coyote$utm.northing)
utm_locations <- SpatialPoints(utm_points,
proj4string=CRS("+proj=utm +zone=18 +datum=WGS84"))
proj_lat.lon <- as.data.frame(spTransform(
utm_locations, CRS("+proj=longlat +datum=WGS84")))
colnames(proj_lat.lon) <- c("x","y")
raster <- openmap(c(max(proj_lat.lon$y)+0.01, min(proj_lat.lon$x)-0.01),
c(min(proj_lat.lon$y)-0.01, max(proj_lat.lon$x)+0.01),
type = "bing")
raster_utm <- openproj(raster,
projection = "+proj=utm +zone=18 +ellps=WGS84 +units=m +no_defs")
autoplot(raster_utm, expand = TRUE) + theme_bw() +
theme(legend.position="bottom") +
theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
geom_point(data=coyote, aes(utm.easting,utm.northing,
color=individual.local.identifier), size = 3, alpha = 0.8) +
theme(axis.title = element_text(face="bold")) + labs(x="Easting",
y="Northing") + guides(color=guide_legend("Identifier"))

Both Beta and Omega had overlaping locations.
Home Range Analysis
Three basic types of home range analyses we will perform in this exercise: Minimum Convex Polygon (MCP), Kernel-Density Estimation (KDE), and Brownian Bridge Movement Model (BB).
MCP - draws the smallest polygon around points with all interior angles less than 180 degrees. MCPs are common estimators of home range, but can potentially include area not used by the animal and overestimate the home range
KDE - calculates the density of features in a neighborhood around those features.
BB - models movement of an individual
MCP Analysis
mcp_raster <- function(filename){
data <- read.csv(file = filename)
x <- as.data.frame(data$utm.easting)
y <- as.data.frame(data$utm.northing)
xy <- c(x,y)
data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS("+proj=utm +zone=18 +ellps=WGS84 +units=m +no_defs"))
xy <- SpatialPoints(data.proj@coords)
mcp.out <- mcp(xy, percent=100, unout="ha")
mcp.points <- cbind((data.frame(xy)),data$individual.local.identifier)
colnames(mcp.points) <- c("x","y", "identifier")
mcp.poly <- fortify(mcp.out, region = "id")
units <- grid.text(paste(round(mcp.out@data$area,2),"ha"), x=0.85, y=0.95,
gp=gpar(fontface=4, col="white", cex=0.9), draw = FALSE)
mcp.plot <- autoplot(raster_utm, expand = TRUE) + theme_bw() + theme(legend.position="none") +
theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
geom_polygon(data=mcp.poly, aes(x=mcp.poly$long, y=mcp.poly$lat), alpha=0.8) +
geom_point(data=mcp.points, aes(x=x, y=y)) +
labs(x="Easting (m)", y="Northing (m)", title=mcp.points$identifier) +
theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) +
annotation_custom(units)
mcp.plot
}
pblapply(files, mcp_raster)
| | 0 % ~calculating
|======================================= | 33% ~01s
|============================================================================== | 67% ~00s
|====================================================================================================================| 100% elapsed=01s
[[1]]
[[2]]
[[3]]



Kernel-Density Estimation
kde_raster <- function(filename){
data <- read.csv(file = filename)
x <- as.data.frame(data$utm.easting)
y <- as.data.frame(data$utm.northing)
xy <- c(x,y)
data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS("+proj=utm +zone=18 +ellps=WGS84 +units=m +no_defs"))
xy <- SpatialPoints(data.proj@coords)
kde<-kernelUD(xy, h="href", kern="bivnorm", grid=100)
ver <- getverticeshr(kde, 95)
kde.points <- cbind((data.frame(data.proj@coords)),data$individual.local.identifier)
colnames(kde.points) <- c("x","y","identifier")
kde.poly <- fortify(ver, region = "id")
units <- grid.text(paste(round(ver$area,2)," ha"), x=0.85, y=0.95,
gp=gpar(fontface=4, col="white", cex=0.9), draw = FALSE)
kde.plot <- autoplot(raster_utm, expand = TRUE) + theme_bw() + theme(legend.position="none") +
theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
geom_polygon(data=kde.poly, aes(x=kde.poly$long, y=kde.poly$lat), alpha = 0.8) +
geom_point(data=kde.points, aes(x=x, y=y)) +
labs(x="Easting (m)", y="Northing (m)", title=kde.points$identifier) +
theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) +
annotation_custom(units)
kde.plot
}
pblapply(files, kde_raster)
| | 0 % ~calculating
|======================================= | 33% ~01s
|============================================================================== | 67% ~00s
|====================================================================================================================| 100% elapsed=01s
[[1]]
[[2]]
[[3]]



Brownian Bridge Movement
No Luck for Now;
couldn’t get crop to run message was: “Error in x@coords[i, , drop = FALSE] : subscript out of bounds”
However, I learned that in excel you need to set the format of the column to custum and type in “yyyy-mm-dd h:mm:ss” and it will correct the entire column to that format. This may be very much a no brainer.
Animate Trajectory Data
Here is my attempt at creating an animation of the individual “Alpha”’s movement from April to July in 2001 in Albany, NY, USA. Data was collected by radio telemetry.
A.move <- move(x=A$location.long,
y=A$location.lat,
time=as.POSIXct(A$timestamp,
format="%Y-%m-%d %H:%M:%S", tz="Asia/Bangkok"),
proj=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84"),
data=A, animal=A$individual.local.identifier,
sensor=A$sensor.type)
movement <- align_move(A.move, res = "max", digit = 0, unit = "secs")
frames <- frames_spatial(movement, path_colours = "orange",
map_service = "osm",
alpha = 0.5) %>%
add_labels(x = "Longitude", y = "Latitude") %>%
add_northarrow() %>%
add_scalebar() %>%
add_timestamps(movement, type = "label") %>%
add_progress()
animate_frames(frames, fps = 5, overwrite = TRUE,
out_file = "./moveVis-5fps.gif")

---
title: "Home Range Crash Course"
author: "Jack Kauphusman"
date: "11/13/2019"
output:
  html_notebook:
    df_print: paged
    highlight: breezedark
    number_sections: yes
    rows.print: 10
    theme: cosmo
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
  html_document:
    df_print: paged
    toc: yes
  pdf_document: default
editor_options:
  chunk_output_type: inline
---
<style type="text/css">

h1.title {
  font-size: 40px;
  font-family: "Times New Roman", Times, serif;
  color: DarkBlue;
  text-align: center;
}
h4.author { /* Header 4 - and the author and data headers use this too  */
  font-size: 20px;
  font-family: "Times New Roman", Times, serif;
  color: DarkBlue;
  text-align: center;
}
</style>
---

```{r Packages, message=FALSE, warning=FALSE, include=FALSE}
packages<-c("adehabitatHR","data.table","ggfortify","grid","move","moveVis","OpenStreetMap","pbapply","plotly","rgdal","sp","tidyverse","viridis")
sapply(packages, require, character.only=T)
library(rJava)
library(OpenStreetMap)
library (raster)
```
# Quick Info

Coyotes (*Canis lantrans*) data on three individuals near Albany, NY, during the spring and summer of 2001, were used as examples for performing home range analyses.

Data comes from:

Bogan, D.A. 2004 Eastern coyote (Canis latrans) home range, habitat selection, and survival rates in the suburban Albany Pine Bush landscape of New York. MA Thesis. State University of New York at Albany.


![](data/image.jpg) 


# Dataset 
```{r}
coyote<-read.csv("data/coyote.csv")
head(coyote)
```

Convert Lat & Long to UTM Because I'm stubborn

```{r}
zone18<-data.frame(coyote$location.long, coyote$location.lat)
names(zone18)<-c("X", "Y")
zone18<-as.matrix(zone18)
UTM<-project(zone18, "+proj=utm +zone=18 ellps=WGS84")
UTM<-data.frame(UTM)
names(UTM)<-c("utm.easting", "utm.northing")
coyote<-cbind(coyote, UTM)
```


Lets see if the dataset has any outliers, and get a rough view of the locations in a map.

```{r}
map1 <- ggplot() + geom_point(data=coyote, 
                                   aes(utm.easting, utm.northing,
                                       color=individual.local.identifier)) +
                        labs(x="Easting", y= "Northing") +
                        guides(color=guide_legend("Identifier"))

ggplotly(map1)

```
Here, I will do a quick map on the three coyote individuals with a basemap from Open Street Map. 

```{r imagery, message=FALSE, warning=FALSE, echo=TRUE, fig.height=6, fig.width=8}
utm_points <- cbind(coyote$utm.easting, coyote$utm.northing)
utm_locations <- SpatialPoints(utm_points, 
                 proj4string=CRS("+proj=utm +zone=18 +datum=WGS84"))
proj_lat.lon <- as.data.frame(spTransform(
                utm_locations, CRS("+proj=longlat +datum=WGS84")))
colnames(proj_lat.lon) <- c("x","y")
raster <- openmap(c(max(proj_lat.lon$y)+0.01, min(proj_lat.lon$x)-0.01), 
                  c(min(proj_lat.lon$y)-0.01, max(proj_lat.lon$x)+0.01), 
                  type = "bing")
raster_utm <- openproj(raster, 
              projection = "+proj=utm +zone=18 +ellps=WGS84 +units=m +no_defs")


autoplot(raster_utm, expand = TRUE) + theme_bw() +
  theme(legend.position="bottom") +
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  geom_point(data=coyote, aes(utm.easting,utm.northing,
             color=individual.local.identifier), size = 3, alpha = 0.8) +
  theme(axis.title = element_text(face="bold")) + labs(x="Easting",
        y="Northing") + guides(color=guide_legend("Identifier"))
```
Both Beta and Omega had overlaping locations.


```{r lapply function, message=FALSE, warning=FALSE, include=FALSE, results='hide'}
lapply(split(coyote, coyote$individual.local.identifier), 
       function(x)write.csv(x, file = paste(x$individual.local.identifier[1],".csv"), row.names = FALSE))
```

```{r list, message=FALSE, warning=FALSE, echo=TRUE, results='hide'}
files <- list.files(path = ".", pattern ="*.csv" , full.names = TRUE)
```

# Home Range Analysis

Three basic types of home range analyses we will perform in this exercise: Minimum Convex Polygon (MCP), Kernel-Density Estimation (KDE), and Brownian Bridge Movement Model (BB).

MCP - draws the smallest polygon around points with all interior angles less than 180 degrees. MCPs are common estimators of home range, but can potentially include area not used by the animal and overestimate the home range

KDE - calculates the density of features in a neighborhood around those features.

BB - models movement of an individual

## MCP Analysis

```{r MCP plot, message=FALSE, warning=FALSE, echo=TRUE, fig.height=6, fig.width=6}
mcp_raster <- function(filename){
  data <- read.csv(file = filename)
  x <- as.data.frame(data$utm.easting)
  y <- as.data.frame(data$utm.northing)
  xy <- c(x,y)
  data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS("+proj=utm +zone=18 +ellps=WGS84 +units=m +no_defs"))
  xy <- SpatialPoints(data.proj@coords)
  mcp.out <- mcp(xy, percent=100, unout="ha")
  mcp.points <- cbind((data.frame(xy)),data$individual.local.identifier)
  colnames(mcp.points) <- c("x","y", "identifier")
  mcp.poly <- fortify(mcp.out, region = "id")
  units <- grid.text(paste(round(mcp.out@data$area,2),"ha"), x=0.85,  y=0.95,
                     gp=gpar(fontface=4, col="white", cex=0.9), draw = FALSE)
  mcp.plot <- autoplot(raster_utm, expand = TRUE) + theme_bw() + theme(legend.position="none") +
    theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
    geom_polygon(data=mcp.poly, aes(x=mcp.poly$long, y=mcp.poly$lat), alpha=0.8) +
    geom_point(data=mcp.points, aes(x=x, y=y)) + 
    labs(x="Easting (m)", y="Northing (m)", title=mcp.points$identifier) +
    theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) + 
    annotation_custom(units)
  mcp.plot
}

pblapply(files, mcp_raster)
```

## Kernel-Density Estimation

```{r KDE plot, message=FALSE, warning=FALSE, echo=TRUE, fig.height=6, fig.width=6}
kde_raster <- function(filename){
  data <- read.csv(file = filename)
  x <- as.data.frame(data$utm.easting)
  y <- as.data.frame(data$utm.northing)
  xy <- c(x,y)
  data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS("+proj=utm +zone=18 +ellps=WGS84 +units=m +no_defs"))
  xy <- SpatialPoints(data.proj@coords)
  kde<-kernelUD(xy, h="href", kern="bivnorm", grid=100)
  ver <- getverticeshr(kde, 95)
  kde.points <- cbind((data.frame(data.proj@coords)),data$individual.local.identifier)
  colnames(kde.points) <- c("x","y","identifier")
  kde.poly <- fortify(ver, region = "id")
  units <- grid.text(paste(round(ver$area,2)," ha"), x=0.85,  y=0.95,
                     gp=gpar(fontface=4, col="white", cex=0.9), draw = FALSE)
  kde.plot <- autoplot(raster_utm, expand = TRUE) + theme_bw() + theme(legend.position="none") +
    theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
    geom_polygon(data=kde.poly, aes(x=kde.poly$long, y=kde.poly$lat), alpha = 0.8) +
    geom_point(data=kde.points, aes(x=x, y=y)) +
    labs(x="Easting (m)", y="Northing (m)", title=kde.points$identifier) +
    theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) + 
    annotation_custom(units)
  kde.plot
}

pblapply(files, kde_raster)
```

### Brownian Bridge Movement

No Luck for Now;

couldn't get crop to run message was: "Error in x@coords[i, , drop = FALSE] : subscript out of bounds"

However, I learned that in excel you need to set the format of the column to custum and type in "yyyy-mm-dd h:mm:ss" and it will correct the entire column to that format. This may be very much a no brainer.

```{r bb plot, eval=FALSE, fig.height=6, fig.width=6, message=FALSE, warning=FALSE, include=FALSE}
A <- read.csv("Alpha .csv")
date <- as.POSIXct(strptime(as.character(A$timestamp),"%Y-%m-%d %H:%M:%S", tz="Asia/Bangkok"))
A$date <- date
A.reloc <- cbind.data.frame(A$utm.easting, A$utm.northing,
                                as.vector(A$individual.local.identifier),
                                as.POSIXct(date))
colnames(A.reloc) <- c("x","y","id","date")
trajectory <- as.ltraj(A.reloc, date=date, id="Alpha")
sig1 <- liker(trajectory, sig2 = 58, rangesig1 = c(0, 5), plotit = FALSE)
opha.traj <- kernelbb(trajectory, sig1 = .7908, sig2 = 58, grid = 100)
bb_ver <- getverticeshr(opha.traj, 95)
bb_poly <- fortify(bb_ver, region = "id", 
                   proj4string = CRS("+proj=utm +zone=18+
                                     ellps=WGS84 +units=m +no_defs"))
colnames(bb_poly) <- c("x","y","order","hole","piece","id","group")
bb_image <- crop(opha.traj, bb_ver, 
                 proj4string = CRS("+proj=utm +zone=47 +
                                   ellps=WGS84 +units=m +no_defs"))
bb_units <- grid.text(paste(round(bb_ver$area,2)," ha"), x=0.85,  y=0.95,
                   gp=gpar(fontface=4, col="white", cex=0.9), draw = FALSE)
bb.plot <- autoplot(raster_utm, expand = TRUE) + theme_bw() + theme(legend.position="none") +
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  geom_tile(data=bb_image, 
            aes(x=bb_image@coords[,1], y=bb_image@coords[,2],
            fill = bb_image@data$ud)) +
  geom_polygon(data=bb_poly, aes(x=x, y=y, group = group), color = "black", fill = NA) +
  scale_fill_viridis_c(option = "inferno") + annotation_custom(bb_units) +
  labs(x="Easting (m)", y="Northing (m)", title="OPHA1") +
  theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5))
bb.plot
```
## Animate Trajectory Data

Here is my attempt at creating an animation of the individual "Alpha"'s movement from April to July in 2001 in Albany, NY, USA. Data was collected by radio telemetry.

```{r move, echo=TRUE, message=FALSE, warning=FALSE, results='hide'}
A.move <- move(x=A$location.long, 
             y=A$location.lat, 
             time=as.POSIXct(A$timestamp, 
                             format="%Y-%m-%d %H:%M:%S", tz="Asia/Bangkok"), 
             proj=CRS("+proj=longlat +ellps=WGS84 +datum=WGS84"),
             data=A, animal=A$individual.local.identifier, 
             sensor=A$sensor.type)

movement <- align_move(A.move, res = "max", digit = 0, unit = "secs")

frames <- frames_spatial(movement, path_colours = "orange",
                         map_service = "osm",
                         alpha = 0.5) %>% 
  add_labels(x = "Longitude", y = "Latitude") %>%
  add_northarrow() %>% 
  add_scalebar() %>% 
  add_timestamps(movement, type = "label") %>% 
  add_progress()

animate_frames(frames, fps = 5, overwrite = TRUE,
               out_file = "./moveVis-5fps.gif")
```
![](./moveVis-5fps.gif "Relocation Animation for Alpha")
